Build Smarter with
Artificial Intelligence

The SKYWORTH Smart PV Industrial Park in Shenzhen, China, has reached a significant construction milestone with the structural topping-out. This signals a major step forward for SKYWORTH's presence in the renewable energy sector, particularly regarding photovoltaic (PV) manufacturing. The expansive industrial park, developed in partnership with SKYWORTH Group Construction & Development and constructed by China Construction Eighth Engineering Division, spans over 63,000 square meters and includes multiple factory buildings and dormitories designed to integrate smart manufacturing principles with green technology. This development offers construction professionals insight into the growing convergence of construction, renewable energy, and advanced manufacturing in large-scale industrial projects.

The park is envisioned as more than just a manufacturing facility; it's intended to be a hub for technology research, intelligent production, and coordinated development across the entire PV value chain. SKYWORTH plans to leverage the park's capabilities to enhance R&D, improve production efficiency, and scale capacity for future expansion. The integration of "PV + inclusive finance + digital technology," as highlighted by SKYWORTH's CIO, suggests a strong reliance on data-driven processes and potentially AI-powered automation within the park's operations. This focus on intelligent manufacturing implies opportunities for construction technology firms to contribute solutions in areas like predictive maintenance, optimized resource allocation, and automated quality control within the solar panel production process.

Construction firms looking to leverage the power of AI need to get their data houses in order, according to a recent report. Buildots' VP of Industry Transformation, Amir Berman, emphasizes that simply throwing money at technology isn't the solution. The real key to success lies in actively managing and standardizing data, turning it into a strategic asset rather than a chaotic byproduct. This shift requires construction professionals to take ownership of their data strategy, demanding structured information as a final deliverable.

The challenge highlighted stems from incompatible data formats, like cost codes in accounting software not aligning with BIM models. This disconnect prevents AI from delivering valuable insights that could optimize project management, reduce costs, and improve efficiency. Berman argues that the construction companies that will thrive in the future will be those that prioritize data standardization and management, enabling AI to unlock its full potential. This means embracing a proactive approach to data governance, rather than relying on technology vendors to solve the problem.

A recent Construction Dive article highlights a critical bottleneck preventing the construction industry from fully leveraging the power of AI: a lack of standardized data. According to a contech executive, the industry's current approach of siloed data collection with inconsistent naming conventions and data structures makes it virtually impossible to glean meaningful insights across projects or portfolios. This lack of standardization hinders the ability to measure and improve processes, effectively trapping the potential value of AI-driven applications for progress tracking, cost analysis, and more.

The author advocates for "active governance," urging construction leaders to prioritize data standardization with the same rigor as safety protocols. This involves taking a proactive role in defining data standards, demanding structured deliverables from technology partners, and measuring data across the entire organization rather than treating each project as an isolated event. By establishing clear guidelines for data collection, organization, and modeling, construction companies can unlock the true potential of AI, enabling them to identify patterns, optimize processes, and ultimately build better and more efficiently. The AI revolution in construction isn't waiting for better models, it's waiting for better data.